Our interest in this paper is in the latter class of loss modification methods.
These have garnered particular interest of late,
with
several recent works [1]}, [2]}, [3]}, [4]}, [5]}, [6]}
establishing the value of enforcing asymmetric logit margins.
Such techniques are intuitive
analogues of the guiding principle behind SVMs,
and aim to clearly separate the scores for rare versus dominant classes.
Despite their success, such techniques are not without limitation.
For example,
when applied to neural
models, they do not explicitly control the distribution
of the learned embeddings themselves.
This can be potentially sub-optimal,
since embeddings for tail classes may be diffuse,
as has been empirically observed [7]}, [8]}, [9]}, [10]}, [11]}, [12]}, [13]}.
